mirror of
https://github.com/ROCm/composable_kernel.git
synced 2026-04-19 22:39:03 +00:00
layernorm & groupnorm bwd gamma beta (#1133)
* Add layernorm bwd gamma beta external api * Add groupnorm external api * Add layernorm bwd gamma beta profiler * Add groupnorm bwd gamma beta ckProfiler * Add layernorm & groupnorm bwd gamma beta test * Fix groupnorm bwd gamma beta profiler bug * Layernorm bwd weight client example * Groupnorm bwd weight client example * clang format * Remove useless header * Let inv_std be positive * Rename to num_bytes and move this calculation outside the loop
This commit is contained in:
rocking
@@ -1,6 +1,9 @@
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add_executable(client_layernorm2d_bwd_data layernorm2d_bwd_data.cpp)
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target_link_libraries(client_layernorm2d_bwd_data PRIVATE composable_kernel::device_other_operations)
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add_executable(client_layernorm2d_bwd_gamma_beta layernorm2d_bwd_gamma_beta.cpp)
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target_link_libraries(client_layernorm2d_bwd_gamma_beta PRIVATE composable_kernel::device_other_operations)
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add_executable(client_layernorm2d_fwd layernorm2d_fwd.cpp)
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target_link_libraries(client_layernorm2d_fwd PRIVATE composable_kernel::device_other_operations)
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171
client_example/05_layernorm/layernorm2d_bwd_gamma_beta.cpp
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171
client_example/05_layernorm/layernorm2d_bwd_gamma_beta.cpp
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@@ -0,0 +1,171 @@
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// SPDX-License-Identifier: MIT
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// Copyright (c) 2018-2023, Advanced Micro Devices, Inc. All rights reserved.
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#include <iomanip>
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#include <vector>
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#include <iostream>
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#include "ck/ck.hpp"
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#include "ck/tensor_operation/gpu/device/device_normalization_bwd_gamma_beta.hpp"
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#include "ck/library/tensor_operation_instance/gpu/layernorm_bwd_gamma_beta.hpp"
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using DYDataType = float;
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using XDataType = float;
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using GammaDataType = float;
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using MeanInvStdDataType = float;
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using DGammaDataType = float;
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using DBetaDataType = float;
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constexpr int Rank = 2;
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constexpr int NumReduceDim = 1;
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struct SimpleDeviceMem
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{
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SimpleDeviceMem() = delete;
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SimpleDeviceMem(std::size_t mem_size) : p_mem_{}
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{
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(void)hipMalloc(static_cast<void**>(&p_mem_), mem_size);
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}
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void* GetDeviceBuffer() { return p_mem_; }
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~SimpleDeviceMem() { (void)hipFree(p_mem_); }
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void* p_mem_;
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};
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int main(int argc, char* argv[])
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{
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ck::index_t M = 1024;
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ck::index_t N = 1024;
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SimpleDeviceMem dy_dev(sizeof(DYDataType) * M * N);
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SimpleDeviceMem x_dev(sizeof(XDataType) * M * N);
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SimpleDeviceMem mean_dev(sizeof(MeanInvStdDataType) * M);
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SimpleDeviceMem inv_std_dev(sizeof(MeanInvStdDataType) * M);
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SimpleDeviceMem dgamma_dev(sizeof(DGammaDataType) * N);
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SimpleDeviceMem dbeta_dev(sizeof(DBetaDataType) * N);
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using DeviceOp =
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ck::tensor_operation::device::DeviceNormalizationBwdGammaBeta<DYDataType,
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XDataType,
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MeanInvStdDataType,
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DGammaDataType,
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DBetaDataType,
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Rank,
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NumReduceDim>;
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// get device op instances
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const auto op_ptrs = ck::tensor_operation::device::instance::DeviceOperationInstanceFactory<
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DeviceOp>::GetInstances();
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std::cout << "found " << op_ptrs.size() << " instances" << std::endl;
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std::string best_op_name;
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bool found = false;
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int best_op_id = -1;
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float best_ave_time = std::numeric_limits<float>::max();
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float best_gb_per_sec = 0;
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// profile device operation instances
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std::cout << "Run all instances and do timing" << std::endl;
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std::size_t num_bytes = sizeof(DYDataType) * M * N + sizeof(XDataType) * M * N +
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sizeof(MeanInvStdDataType) * M * 2 + sizeof(DGammaDataType) * N +
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sizeof(DBetaDataType) * N;
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for(int i = 0; i < op_ptrs.size(); ++i)
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{
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auto& op_ptr = op_ptrs[i];
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auto argument_ptr = op_ptr->MakeArgumentPointer({M, N}, // inLengths
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{N, 1}, // dyStrides
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{N, 1}, // xStrides
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{1, 0}, // meanStrides
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{1, 0}, // invStdStrides
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{N}, // outLengths
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{1}, // dgammaStrides
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{1}, // dbetaStrides
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{0}, // reduceDims
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dy_dev.GetDeviceBuffer(),
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x_dev.GetDeviceBuffer(),
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mean_dev.GetDeviceBuffer(),
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inv_std_dev.GetDeviceBuffer(),
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dgamma_dev.GetDeviceBuffer(),
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dbeta_dev.GetDeviceBuffer());
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auto invoker_ptr = op_ptr->MakeInvokerPointer();
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std::string op_name = op_ptr->GetTypeString();
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if(op_ptr->IsSupportedArgument(argument_ptr.get()))
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{
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size_t workspace_sz = op_ptr->GetWorkSpaceSize(argument_ptr.get());
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SimpleDeviceMem workspace(workspace_sz);
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op_ptr->SetWorkSpacePointer(argument_ptr.get(), workspace.GetDeviceBuffer());
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float ave_time = invoker_ptr->Run(argument_ptr.get(), StreamConfig{nullptr, true});
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float gb_per_sec = num_bytes / 1.E6 / ave_time;
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std::cout << "Perf: " << std::setw(10) << ave_time << " ms, " << gb_per_sec << " GB/s, "
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<< op_name << std::endl;
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if(ave_time < best_ave_time)
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{
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found = true;
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best_op_id = i;
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best_op_name = op_name;
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best_ave_time = ave_time;
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best_gb_per_sec = gb_per_sec;
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}
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}
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else
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{
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std::cout << op_name << " does not support this problem" << std::endl;
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}
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}
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std::cout << "Best Perf: " << best_ave_time << " ms, " << best_gb_per_sec << " GB/s, "
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<< best_op_name << std::endl;
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// run the best intance
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if(found)
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{
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auto& op_ptr = op_ptrs[best_op_id];
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std::cout << "Run the best instance without timing: " << op_ptr->GetTypeString()
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<< std::endl;
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auto argument_ptr = op_ptr->MakeArgumentPointer({M, N}, // inLengths
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{N, 1}, // dyStrides
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{N, 1}, // xStrides
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{1, 0}, // meanStrides
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{1, 0}, // invStdStrides
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{N}, // outLengths
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{1}, // dgammaStrides
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{1}, // dbetaStrides
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{0}, // reduceDims
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dy_dev.GetDeviceBuffer(),
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x_dev.GetDeviceBuffer(),
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mean_dev.GetDeviceBuffer(),
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inv_std_dev.GetDeviceBuffer(),
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dgamma_dev.GetDeviceBuffer(),
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dbeta_dev.GetDeviceBuffer());
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auto invoker_ptr = op_ptr->MakeInvokerPointer();
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if(op_ptr->IsSupportedArgument(argument_ptr.get()))
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{
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size_t workspace_sz = op_ptr->GetWorkSpaceSize(argument_ptr.get());
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SimpleDeviceMem workspace(workspace_sz);
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op_ptr->SetWorkSpacePointer(argument_ptr.get(), workspace.GetDeviceBuffer());
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invoker_ptr->Run(argument_ptr.get(), StreamConfig{nullptr, false});
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}
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std::cout << "Done" << std::endl;
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}
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return 0;
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}
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@@ -1,5 +1,8 @@
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add_executable(client_groupnorm_bwd_data groupnorm_bwd_data.cpp)
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target_link_libraries(client_groupnorm_bwd_data PRIVATE composable_kernel::device_other_operations)
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add_executable(client_groupnorm_bwd_gamma_beta groupnorm_bwd_gamma_beta.cpp)
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target_link_libraries(client_groupnorm_bwd_gamma_beta PRIVATE composable_kernel::device_other_operations)
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add_executable(client_groupnorm_swish_fwd groupnorm_swish_fwd.cpp)
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target_link_libraries(client_groupnorm_swish_fwd PRIVATE composable_kernel::device_other_operations)
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180
client_example/18_groupnorm/groupnorm_bwd_gamma_beta.cpp
Normal file
180
client_example/18_groupnorm/groupnorm_bwd_gamma_beta.cpp
Normal file
@@ -0,0 +1,180 @@
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// SPDX-License-Identifier: MIT
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// Copyright (c) 2018-2023, Advanced Micro Devices, Inc. All rights reserved.
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#include <iomanip>
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#include <vector>
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#include <iostream>
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#include "ck/ck.hpp"
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#include "ck/tensor_operation/gpu/device/device_normalization_bwd_gamma_beta.hpp"
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#include "ck/library/tensor_operation_instance/gpu/groupnorm_bwd_gamma_beta.hpp"
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using DYDataType = float;
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using XDataType = float;
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using GammaDataType = float;
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using MeanInvStdDataType = float;
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using DGammaDataType = float;
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using DBetaDataType = float;
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constexpr int Rank = 5;
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constexpr int NumReduceDim = 3;
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struct SimpleDeviceMem
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{
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SimpleDeviceMem() = delete;
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SimpleDeviceMem(std::size_t mem_size) : p_mem_{}
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{
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(void)hipMalloc(static_cast<void**>(&p_mem_), mem_size);
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}
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void* GetDeviceBuffer() { return p_mem_; }
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~SimpleDeviceMem() { (void)hipFree(p_mem_); }
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void* p_mem_;
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};
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int main(int argc, char* argv[])
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{
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ck::index_t N = 32;
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ck::index_t H = 16;
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ck::index_t W = 16;
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ck::index_t G = 64;
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ck::index_t C = 128;
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std::size_t length = N * H * W * G * C;
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std::vector<ck::index_t> strideDy = {H * W * G * C, W * G * C, G * C, C, 1};
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std::vector<ck::index_t> strideX = strideDy;
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std::vector<ck::index_t> strideMeanInvStd = {G, 0, 0, 1, 0};
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std::vector<ck::index_t> strideDGammaBeta = {C, 1};
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SimpleDeviceMem dy_dev(sizeof(DYDataType) * length);
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SimpleDeviceMem x_dev(sizeof(XDataType) * length);
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SimpleDeviceMem mean_dev(sizeof(MeanInvStdDataType) * N * G);
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SimpleDeviceMem inv_std_dev(sizeof(MeanInvStdDataType) * N * G);
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SimpleDeviceMem dgamma_dev(sizeof(DGammaDataType) * G * C);
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SimpleDeviceMem dbeta_dev(sizeof(DBetaDataType) * G * C);
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using DeviceOp =
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ck::tensor_operation::device::DeviceNormalizationBwdGammaBeta<DYDataType,
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XDataType,
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MeanInvStdDataType,
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DGammaDataType,
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DBetaDataType,
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Rank,
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NumReduceDim>;
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// get device op instances
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const auto op_ptrs = ck::tensor_operation::device::instance::DeviceOperationInstanceFactory<
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DeviceOp>::GetInstances();
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std::cout << "found " << op_ptrs.size() << " instances" << std::endl;
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std::string best_op_name;
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bool found = false;
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int best_op_id = -1;
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float best_ave_time = std::numeric_limits<float>::max();
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float best_gb_per_sec = 0;
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// profile device operation instances
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std::cout << "Run all instances and do timing" << std::endl;
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std::size_t num_bytes = sizeof(DYDataType) * length + sizeof(XDataType) * length +
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sizeof(GammaDataType) * G * C + sizeof(MeanInvStdDataType) * N * G * 2 +
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sizeof(DGammaDataType) * G * C + sizeof(DBetaDataType) * G * C;
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for(int i = 0; i < op_ptrs.size(); ++i)
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{
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auto& op_ptr = op_ptrs[i];
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auto argument_ptr = op_ptr->MakeArgumentPointer({N, H, W, G, C},
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strideDy,
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strideX,
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strideMeanInvStd,
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strideMeanInvStd,
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{G, C},
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strideDGammaBeta,
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strideDGammaBeta,
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{0, 1, 2}, // reduceDims
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dy_dev.GetDeviceBuffer(),
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x_dev.GetDeviceBuffer(),
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mean_dev.GetDeviceBuffer(),
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inv_std_dev.GetDeviceBuffer(),
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dgamma_dev.GetDeviceBuffer(),
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dbeta_dev.GetDeviceBuffer());
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auto invoker_ptr = op_ptr->MakeInvokerPointer();
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std::string op_name = op_ptr->GetTypeString();
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if(op_ptr->IsSupportedArgument(argument_ptr.get()))
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{
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size_t workspace_sz = op_ptr->GetWorkSpaceSize(argument_ptr.get());
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SimpleDeviceMem workspace(workspace_sz);
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op_ptr->SetWorkSpacePointer(argument_ptr.get(), workspace.GetDeviceBuffer());
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float ave_time = invoker_ptr->Run(argument_ptr.get(), StreamConfig{nullptr, true});
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float gb_per_sec = num_bytes / 1.E6 / ave_time;
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std::cout << "Perf: " << std::setw(10) << ave_time << " ms, " << gb_per_sec << " GB/s, "
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<< op_name << std::endl;
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if(ave_time < best_ave_time)
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{
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found = true;
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best_op_id = i;
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best_op_name = op_name;
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best_ave_time = ave_time;
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best_gb_per_sec = gb_per_sec;
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}
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}
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else
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{
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std::cout << op_name << " does not support this problem" << std::endl;
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}
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}
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// run the best intance
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if(found)
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{
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std::cout << "Best Perf: " << best_ave_time << " ms, " << best_gb_per_sec << " GB/s, "
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<< best_op_name << std::endl;
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auto& op_ptr = op_ptrs[best_op_id];
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std::cout << "Run the best instance without timing: " << op_ptr->GetTypeString()
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<< std::endl;
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auto argument_ptr = op_ptr->MakeArgumentPointer({N, H, W, G, C},
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strideDy,
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strideX,
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strideMeanInvStd,
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strideMeanInvStd,
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{G, C},
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strideDGammaBeta,
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strideDGammaBeta,
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{0, 1, 2}, // reduceDims
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dy_dev.GetDeviceBuffer(),
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x_dev.GetDeviceBuffer(),
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mean_dev.GetDeviceBuffer(),
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inv_std_dev.GetDeviceBuffer(),
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dgamma_dev.GetDeviceBuffer(),
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dbeta_dev.GetDeviceBuffer());
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auto invoker_ptr = op_ptr->MakeInvokerPointer();
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if(op_ptr->IsSupportedArgument(argument_ptr.get()))
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{
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size_t workspace_sz = op_ptr->GetWorkSpaceSize(argument_ptr.get());
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SimpleDeviceMem workspace(workspace_sz);
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op_ptr->SetWorkSpacePointer(argument_ptr.get(), workspace.GetDeviceBuffer());
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invoker_ptr->Run(argument_ptr.get(), StreamConfig{nullptr, false});
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}
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std::cout << "Done" << std::endl;
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}
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return 0;
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}
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